Get a gist of how AI-based QA evaluations work and how to drive its success through your scorecards.
This article supports Super User and Supervisor roles only.
Current manual QA processes depend on human experience for results but it does take a while to process a high volume of customer-agent interactions. To improve the QA process, PeopleQX uses AI to evaluate customer-agent interactions, or what we call AutoQA evaluations.
To get the best out of AutoQA, the scorecard needs to be crafted in a way that enables the AI model to understand and score accurately. Manual scorecards may contain open-ended questions or questions subject to human deduction — it would be challenging for AI to provide an accurate result. You’ll likely need to rewrite questions following the parameters for AI scoring, including:
- Specificity
- Preciseness
- Context
- Availability of examples
- Drives towards binary answers (Yes/No)
Writing questions for AutoQA may be a learning curve for some but once you get the hang of the principles and techniques, it should be smooth sailing.
PeopleQX also ensures the success of your scorecards with a scorecard simulator. It allows a builder to test the effectiveness of each question and refine it until it achieves a satisfactory score. If you have crafted a high-quality question, you can also save it to be shared with other users, thus speeding up the process of developing future scorecards.
Confident that scorecards optimized for PeopleQX will improve the QA experience and the quality of CX? If you’re ready to get started, here’s where to find the scorecard builder.